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International Journal of Management (IJM) Volume 6, Issue 10, Oct 2015, pp. 150-170, Article ID: IJM_06_10_018 Available online at http://www.iaeme.com/IJM/issues.asp?JType=IJM&VType=6&IType=10 ISSN Print: 0976-6502 and ISSN Online: 0976-6510 © IAEME Publication ___________________________________________________________________________ CHANGING DIMENSIONS OF SHOPPING PREFERENCES IN NAGPUR CITY Dr. Nirzar Kulkarni Professor and Dean (Admin and Admissions) Dr. Ambedkar Institute of Management Studies and Research, Deekshabhoomi, VIP Road, Nagpur-10 ABSTRACT In the consumer packaged goods (CPG) industry, change has been more evolutionary than innovative, but digital is redefining what it means to “go for” shopping. Distances between the physical and digital worlds are distorting. Shoppers are increasing familiar to the benefits of digital in other retail settings and are ready to expect them in routine shopping. Savvy retailers are captivated by leveraging technology to improve the shopping experience and meet consumers’ budding desires. “Consumers are no longer shopping completely online or offline; rather, they’re taking a mixed method, using whatsoever channel finest suits their needs. The most effective retailers and manufacturers will be at the juncture of the physical and virtual worlds, leveraging technology to please shoppers conversely, anywhere and whenever they want to shop.” The researcher has surveyed 200online respondents from 37 prime areas from Nagpur city and out of these 37 areas 6 respondents from each area were selected on the cluster random sampling basis and the information is collected through the questionnaire. The major concern of the study was to understand how digital technologies will shape the retail landscape of the future. The researcher has focused on how consumers are using technology and offer insights about how retailers and manufacturers can use flexible retailing options to improve the shopping experience and initiate increased visits and sales across different channels. The researcher has also tried to examine how distribution and channel shopping preferences are changing. Cite this Article: Dr. Nirzar Kulkarni. Changing Dimensions of Shopping Preferences In Nagpur City, International Journal of Management, 6(10), 2015, pp. 150-170. http://www.iaeme.com/IJM/issues.asp?JType=IJM&VType=6&IType=10 http://www.iaeme.com/IJM/index.asp 150 editor@iaeme.com Changing Dimensions of Shopping Preferences In Nagpur City INTRODUCTION Electronic commerce or ecommerce is a term for any type of business, or commercial transaction, that involves the transfer of information across the Internet. It covers a range of different types of businesses, from consumer based retail sites, through auction or music sites, to business exchanges trading goods and services between corporations. It is currently one of the most important aspects of the Internet to emerge. Ecommerce allows consumers to electronically exchange goods and services with no barriers of time or distance. Electronic commerce has expanded rapidly over the past five years and is predicted to continue at this rate, or even accelerate. In the near future the boundaries between "conventional" and "electronic" commerce will become increasingly blurred as more and more businesses move sections of their operations onto the Internet. CATEGORIES OF E-COMMERCE As with traditional commerce, there are four principal categories of e-commerce: B2B, B2C, C2B and C2C.  B2B (Business to Business) This involves companies doing business with each other. One example is manufacturers selling to distributors and wholesalers selling to retailers.  B2C (Business to Consumer) B2C consists of businesses selling to the general public through shopping cart software, without needing any human interaction. This is what most people think of when they hear "e-commerce." An example of this would be Amazon.  C2B (Consumer to Business) In C2B e-commerce, consumers post a project with a set budget online, and companies bid on the project. The consumer reviews the bids and selects the company. Elance is an example of this. C2C (Consumer to Consumer) This takes place within online classified ads, forums or marketplaces where individuals can buy and sell their goods. Examples of this include Craigslist, eBay and Etsy.  DIGITAL TECHNOLOGY AND CHANGING SHOPPING PREFERENCES Consumer no longer see a distinction between online and offline shopping. Whether it’s searching on a laptop, browsing main street shops or hanging out at the mall — it’s all shopping. To adapt to the competitive new reality, smart retailers are drawing on classic retailing truths of the past and augmenting them for the now. Innovative retailers are embracing this new reality, using digital to extend their storefronts. These are my top five observations on how shopping has changed and suggestions for how marketers can adapt to join the retail revolution. SHOPPERS KNOW AS MUCH AS SALESPEOPLE Then: People came into stores with little to no knowledge and relied on a salesperson to advise them on what to buy. http://www.iaeme.com/IJM/index.asp 151 editor@iaeme.com Dr. Nirzar Kulkarni Now: Today’s shoppers have become accustomed to doing their own research to get the maximum value out of every dollar they spend, and to feel secure about the purchases they’re making. With this power shift comes a great opportunity for retailers; those that use tools and insights from the web have the opportunity to close the gap between the smart online consumer and the offline retailer, and to stand out in a competitive marketplace. Every moment in a consumer’s decision journey matters. To win these moments, smart retailers need to be there when inspiration strikes consumers and as they start researching purchases online. RETAILERS CAN DELIVER PERSONAL, RELEVANT SUGGESTIONS AT SCALE Then: Retailing began with shopkeepers who would welcome in people from the neighborhood and then come to learn their customers’ needs and preferences. Now: In our constantly connected world, a device is just a proxy for what really matters — getting to know your customers. Devices provide context, helping us learn what matters to a consumer in a particular location and at a particular time. Coupled with the intent provided by search, this is incredibly powerful. It can help retailers deliver relevant suggestions, essentially recreating those shopkeeper conversations at scale. The right message at the right moment is the next level in customer service — it can quickly and easily turn intent into action. Context also allows retailers to better than ever anticipate what a customer might need based on when, where and how they arrive at their site and help them decide how to respond to them. People are constantly looking for product information, deals, local availability and local discounts online — and retailers who aren’t there to supply the right information when people raise their virtual hand will lose out. MOBILE DEVICES DRIVE FOOT TRAFFIC TO STORES Then: Finding the right store — and the product you needed — depended on familiarity, or serendipity. Now: As the lines blur between online and offline, innovative retailers are integrating mobile into their brick-and-mortar store experience. When shoppers search for a store name or category, they expect to see a map with directions, a phone number that they can easily click-to-call, or special offers that match their location and time of day. Adidas worked with their agency iProspect to evaluate how mobile clicks on their store locator links were driving in-store sales, and found that for a mobile investment of $1 million, the value brought by store locator clicks in mobile ads generated an extra $1.6 million in sales. The search element of shopping doesn’t end once the customer walks into a store. At some point, we’ve all been lost in the supermarket, searching the aisles for an http://www.iaeme.com/IJM/index.asp 152 editor@iaeme.com Changing Dimensions of Shopping Preferences In Nagpur City elusive item. Mobile can be a map, a shopping list, a personal shopper, a salesperson and a product finder all at once. OPINIONS CARRY MORE WEIGHT THAN EVER Then: Retail therapy was an activity shared by friends and family — and word of mouth was a social force that transformed new products into must-haves and small shops into retail empires. Now: This is truer than ever. With YouTube and social networks like G+, people are now sharing their opinion on products not just with a group of friends, but with millions of people. This is why Google Shopping incorporates reviews and introduced shortlists to make it easy for people to discuss products and purchases with friends and family. Smart retailers are recognizing the opportunities that lie in digital where instead of basing campaigns on the broadest reach possible they can now zero in and speak directly with the individuals, or communities of fans, who love their products most. Retailers are also seizing the opportunities around online comments by advertising against terms like “reviews” and working to promote the positive and counteract the negative. PRODUCTS CAN JUMP OFF THE SCREEN Then: The internet was fine for researching, but there was no replacement for holding, feeling, inspecting a physical product on a store shelf or showroom floor. Now: Interactive video, 360 views, gestural controls are just a few of the options bringing products alive on customers’ multiple screens. Each digital stride opens exciting opportunities to close the gap between an on-screen image and that experience of holding a product in a store. Google Shopping has introduced 360-degree imagery to some product sets to give shoppers a better sense of what an item really looks like. Some innovative retailers are even offering shoppers virtual try-ons. Eye-glasses retailer Warby Parker, for example, invites customers to mix and match frames against their photo. When retailers showcase products online in a unique way, they create opportunities for customers to interact with products on an emotional level. When consumers’ emotions are activated, their desire to buy is sparked. We are only beginning to see the possibilities. A device is just a proxy for what really matters — getting to know your customers. Devices provide context, helping us learn what matters to a consumer in a particular location and at a particular time. As digital weaves itself deeper into the fabric of our lives, smart retailers understand making the most of new opportunities is not about gadgets or technology. It’s about human nature. Forward-thinking retailers should be looking at how they are interweaving digital tools like mobile, context, and video with sales, marketing and customer service. When these things are used well, the technology becomes invisible. http://www.iaeme.com/IJM/index.asp 153 editor@iaeme.com Dr. Nirzar Kulkarni Customers simply see retailers as being very good at giving them exactly what they want. (Source: https://www.thinkwithgoogle.com/articles/five-ways-retail-has-changedand-how-businesses-can-adapt.html) In order to study these changing shopping preferences in Nagpur city this study has been undertaken. METHODOLOGY In this study the respondents were categorized into 5 categories as follows: 1. 2. 3. 4. 5. Generation Z : between the age group of 15-20 years Millennials : between the age group of 21-34 years Generation X : between the age group of 35-49 years Baby Boomers : between the age group of 50-64 years Silent Generation: above 65 years Universe of the study: Nagpur has a population of 4.6 million and it is the 13th largest urban conglomeration in India, according to figures from the 2001 census. Sampling technique: the sampling technique adopted for this study was cluster random sampling. Cluster sampling is a sampling technique used when "natural" but relatively homogeneous groupings are evident in a statistical population. It is often used in marketing research. In this technique, the total population is divided into these groups (or clusters) and a simple random sample of the groups is selected. Then the required information is collected from a simple random sample of the elements within each selected group. This may be done for every element in these groups or a subsample of elements may be selected within each of these groups. A common motivation for cluster sampling is to reduce the total number of interviews and costs given the desired accuracy. Sample size: out of the total population of the Nagpur city, specific areas from all the parts of the city were selected for the study, and out of those selected areas 6 respondents from each area were taken as a sample. i.e 37 areas x 6 respondents per area = 222 total respondents Questionnaire was distributed to all these 222 respondents and actual response were gathered from 200 and 22 questionnaires were rejected since those were not completely filled up. Localities: S.No. Localities 1 2 3 4 5 6 7 8 9 No. of respondents 6 6 6 6 6 6 6 6 6 Dhantoli Itwari Sitabuldi Mominpura Dharampeth Sadar Civil Lines Gandhibagh Mahal http://www.iaeme.com/IJM/index.asp 154 editor@iaeme.com Changing Dimensions of Shopping Preferences In Nagpur City S.No. Localities 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 No. of respondents 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 222 Nandanvan Kalamna Wardhaman Nagar Seminary Hills Police Line Takli Mankapur Pachpaoli Vayusena Nagar Ravi Nagar Byramji Town Chaoni Mangalwari GaddiGodam GittiKhadan Pratap Nagar Ajni Pardi Indora Maskasath Jaripatka Ashok Nagar Gokulpeth Giripeth Bajaj Nagar Rajendra Nagar Lakadganj Gandhinagar Manish Nagar Total (Source: https://en.wikipedia.org/wiki/List_of_localities_in_Nagpur) Data Collection: This study is based on the primary data collection majorly. Hypothesis: H01: there is a strong association between age and willingness to use E-commerce options. H02: there is a strong association between age and use of E-commerce options. H03: there is a strong association between gender and use of E-commerce options. H04: there is a strong association between age and preferred stock-up products. H05: there is a strong association between age and attributes which drives the users to switch stores. http://www.iaeme.com/IJM/index.asp 155 editor@iaeme.com Dr. Nirzar Kulkarni Test of Hypothesis: H1: there is a strong association between age and use of E-commerce options. To test the above hypothesis chi square test is used. The chi-square test for independence, also called Pearson's chi-square test or the chi-square test of association, is used to discover if there is aassociation between two categorical variables. 15-20 Age 21-34 35-49 50-64 64+ Total Age * Ecom User Cross tabulation Ecom User Already user willing to use Count 23 9 % within Age 71.9% 28.1% % within Ecom User 38.3% 6.4% % of Total 11.5% 4.5% Count 0 32 % within Age 0.0% 100.0% % within Ecom User 0.0% 22.9% % of Total 0.0% 16.0% Count 0 70 % within Age 0.0% 100.0% % within Ecom User 0.0% 50.0% % of Total 0.0% 35.0% Count 29 19 % within Age 60.4% 39.6% % within Ecom User 48.3% 13.6% % of Total 14.5% 9.5% Count 8 10 % within Age 44.4% 55.6% % within Ecom User 13.3% 7.1% % of Total 4.0% 5.0% Count 60 140 % within Age 30.0% 70.0% % within Ecom User 100.0% 100.0% % of Total 30.0% 70.0% Total 32 100.0% 16.0% 16.0% 32 100.0% 16.0% 16.0% 70 100.0% 35.0% 35.0% 48 100.0% 24.0% 24.0% 18 100.0% 9.0% 9.0% 200 100.0% 100.0% 100.0% The above table helps to understand that in every age group there are people who are already the users of E-commerce and also the people who are willing to use Ecommerce options. Chi-Square Tests Pearson Chi-Square Likelihood Ratio 0.9370a 117.147 4 4 Asymp. Sig. (2sided) .000 .000 Linear-by-Linear Association .115 1 .735 Value df N of Valid Cases 200 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 5.40. When reading this table we are interested in the results of the "Pearson ChiSquare" row. We can see here that χ(1) = 0.937, p = .000. This tells us that there is a statistically significant association between age and use of E-commerce options. http://www.iaeme.com/IJM/index.asp 156 editor@iaeme.com Changing Dimensions of Shopping Preferences In Nagpur City Symmetric Measures Value Approx. Sig. Phi .683 .000 Nominal by Nominal Cramer's V .683 .000 N of Valid Cases 200 a. Not assuming the null hypothesis. b. Using the asymptotic standard error assuming the null hypothesis. Phi and Cramer's V are both tests of the strength of association. We can see that the strength of association between Baby boomers and use of E-commerce options is strong. Hence from this we can say that the alternate hypothesis H1: there is a strong association between Baby boomers and use of E-commerce options is accepted. H2: there is a strong association between age and preferred E-commerce options. To test the above hypothesis chi square test is used. The chi-square test for independence, also called Pearson's chi-square test or the chi-square test of association, is used to discover if there is aassociation between two categorical variables. Age * preferred ecom options Cross tabulation Preferred ecom options Order Order Use a Use online online and online for virtual automatic pick up delivery to supermark subscription inside the home et store Total Count 24 8 0 0 32 % within Age 75.0% 25.0% 0.0% 0.0% 100.0% % within preferred ecom 80.0% options 14.3% 0.0% 0.0% 16.0% % of Total 12.0% 4.0% 0.0% 0.0% 16.0% Count 0 32 0 0 32 % within Age 0.0% 100.0% 0.0% 0.0% 100.0% % within preferred ecom 0.0% options 57.1% 0.0% 0.0% 16.0% % of Total 0.0% 16.0% 0.0% 0.0% 16.0% Count 0 16 41 13 70 % within Age 0.0% 22.9% 58.6% 18.6% 100.0% % within preferred ecom 0.0% options 28.6% 100.0% 17.8% 35.0% % of Total 0.0% 8.0% 20.5% 6.5% 35.0% http://www.iaeme.com/IJM/index.asp 157 15-20 Age 21-34 35-49 editor@iaeme.com Dr. Nirzar Kulkarni Age * preferred ecom options Cross tabulation Preferred ecom options Order Order Use a Use online online and online for virtual automatic pick up delivery to supermark subscription inside the home et store 50-64 64+ Total Total Count 0 0 0 48 48 % within Age 0.0% 0.0% 0.0% 100.0% 100.0% % within preferred ecom 0.0% options 0.0% 0.0% 65.8% 24.0% % of Total 0.0% 0.0% 0.0% 24.0% 24.0% Count 6 0 0 12 18 % within Age 33.3% 0.0% 0.0% 66.7% 100.0% % within preferred ecom 20.0% options 0.0% 0.0% 16.4% 9.0% % of Total 3.0% 0.0% 0.0% 6.0% 9.0% Count 30 56 41 73 200 % within Age 15.0% 28.0% 20.5% 36.5% 100.0% % within preferred ecom 100.0% options 100.0% 100.0% 100.0% 100.0% % of Total 28.0% 20.5% 36.5% 100.0% 15.0% The above table helps to understand that in every age group there are people who preference of E-commerce options for different purpose. Chi-Square Tests Value df Asymp. Sig. (2sided) .000 .000 Pearson Chi-Square 0.9700a 12 Likelihood Ratio 339.727 12 Linear-by-Linear 106.634 1 .000 Association N of Valid Cases 200 a. 4 cells (20.0%) have expected count less than 5. The minimum expected count is 2.70. When reading this table we are interested in the results of the "Pearson ChiSquare" row. We can see here that χ(1) = 0.97, p = .000. This tells us that there is a statistically significant association between Age and preference of E-commerce options for different purpose. http://www.iaeme.com/IJM/index.asp 158 editor@iaeme.com Changing Dimensions of Shopping Preferences In Nagpur City Symmetric Measures Value Approx. Sig. Phi 1.313 .000 Nominal by Nominal Cramer's V .758 .000 N of Valid Cases 200 a. Not assuming the null hypothesis. b. Using the asymptotic standard error assuming the null hypothesis. Phi and Cramer's V are both tests of the strength of association. We can see that the strength of association between age and preferred e-com options is very strong. Hence from this we can say that the alternate hypothesis H2: there is a strong association between age and preferred E-commerce options is accepted. H3: there is a strong association between gender and use of E-commerce options. To test the above hypothesis chi square test is used. The chi-square test for independence, also called Pearson's chi-square test or the chi-square test of association, is used to discover if there is aassociation between two categorical variables. Gender Total Gender * preferred ecom options Cross tabulation Preferred ecom options Order online Order online Use online Use a virtual and pick up for delivery automatic supermarket inside the to home subscription store Count 24 56 4 20 % within 23.1% 53.8% 3.8% 19.2% Gender Male % within preferred 80.0% 100.0% 9.8% 27.4% ecom options % of Total 12.0% 28.0% 2.0% 10.0% Count 6 0 37 53 % within 6.2% 0.0% 38.5% 55.2% Gender Female % within preferred 20.0% 0.0% 90.2% 72.6% ecom options % of Total 3.0% 0.0% 18.5% 26.5% Count 30 56 41 73 % within 15.0% 28.0% 20.5% 36.5% Gender % within preferred 100.0% 100.0% 100.0% 100.0% ecom options % of Total 15.0% 28.0% 20.5% 36.5% Total 104 100.0% 52.0% 52.0% 96 100.0% 48.0% 48.0% 200 100.0% 100.0% 100.0% The above table helps to understand that gender wise the preference of Ecommerce options for different purpose also changes. http://www.iaeme.com/IJM/index.asp 159 editor@iaeme.com